Recent advances in camera technology and increased processor power have enabled surveillance systems to monitor human activities in real-time. (See, for example, D. M. Gavrila, “The visual analysis of human movement: a survey”, Computer Vision and Image Understanding, vol. 73, pp. 82-98, 1999.) Surveillance cameras are commonly used in stores, hallways, and along streets for security applications such as crime-prevention or post-crime investigations. On the other hand, retail store managers are also interested in using cameras to extract business intelligence information. Retailers desire real-time information about customer traffic patterns, queue lengths, and check-out waiting times to improve operational efficiency and customer satisfaction. They are more interested in determining the number of shopping groups within their stores than the total number of people who frequent them.
There have been a number of studies on detecting and tracking people. (See, for example, D. Beymer et al., “Real-time tracking of multiple people using continuous detection,” IEEE Frame Rate Workshop, Cofu, Greece, 1999; J. W. Davis et al., “The representation and recognition of action using temporal templates,” IEEE Conference on Computer Vision and Pattern Recognition, June 1997; T. Darrell et al., “Integrated person tracking using stereo, color, and pattern detection,” IEEE Computer Vision and Pattern Recognition Conference, Santa Barbara, Calif., 1998; K. Waters et al., “Visual sensing of humans for active public interfaces,” Digital Equipment Corporation/Cambridge Research Lab Technical Report CRL 96/5, March 1996; R. Rosales et al., “3-D trajectory recovery for tracking multiple objects and trajectory guided recognition of actions,” Proceedings IEEE Conference on Computer Vision and Pattern Recognition, June 1999; R. T. Collins et al., “A system for video surveillance and monitoring,” VSAM Final Technical Report, CMU-RI-TR-00-12, Robotics Institute, Carnegie Mellon University, May 2000; C. Wren et al., “Pfinder: real-time tracking of the human body,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, pp. 780-785, July 1997.) However, these investigators have not addressed the issue of detecting and locating people as they move as a group.
The ability to visually detect and track groups of people and their activities in crowded areas therefore remains an important problem. This problem is complicated by the fact that when people interact with each other, they are often partially or totally occluded.